Required R packages and Directories

data.dir = 'https://mdporter.github.io/SYS6018/data/' # data directory
library(R6018)     # functions for SYS-6018
library(tidyverse) # functions for data manipulation   
library(mlbench)
library(glmnet)
library(glmnetUtils)

Load the focal plane training dataset

focal_url = "http://localhost/Data/spectro_nn/focalPlane/Equal/EqEvt731/order10_ep5/combine.csv" 
data_focal = readr::read_csv(focal_url)

data_focal$focal_x  = data_focal$x1th0y0ph0
data_focal$focal_th = data_focal$x0th1y0ph0
data_focal$focal_y  = data_focal$x0th0y1ph0
data_focal$focal_ph = data_focal$x0th0y0ph1
# head(data_focal)

check the focal plane \(\phi\) parameter

runList = c(2239,2240,2241)
result.ph = tibble(runID = numeric(), index = numeric(), aver_X = numeric(), aver_Y = numeric())

colors <- c("col.4"="black", "col.6"="red", "col.7"="blue")

myplots <- list()  # new empty list
for (indexID in 1:length(runList)){
  runID.step = runList[indexID] 
  
  data.step = data_focal %>% filter(runID == runID.step)  %>% filter(SieveColID == 4 | SieveColID == 6 | SieveColID == 7)
  data63 = data.step %>% filter(SieveColID == 6 & SieveRowID == 3)
  center.63 =  tibble(x= mean(data63$focal_ph), y = mean(data63$focal_th))
  
  data42 = data.step %>% filter(SieveColID == 4 & SieveRowID == 2)
  center.42 =  tibble(x= mean(data42$focal_ph), y = mean(data42$focal_th))
  data72 = data.step %>% filter(SieveColID == 7 & SieveRowID == 2)
  center.72 = tibble(x= mean(data72$focal_ph), y = mean(data72$focal_th))

  result.ph = add_row(result.ph,tibble(runID = runID.step, index = 63, aver_X = center.63$x, aver_Y = center.63$y))
  result.ph = add_row(result.ph,tibble(runID = runID.step, index = 42, aver_X = center.42$x, aver_Y = center.42$y))
  result.ph = add_row(result.ph,tibble(runID = runID.step, index = 72, aver_X = center.72$x, aver_Y = center.72$y))
  
  myp = ggplot(data.step) + geom_bin2d(aes(x=focal_ph,y=focal_th,color = sprintf("col.%d",SieveColID)),bins=300) + geom_point(data = center.63, aes(x= x , y =y , color = "data.63")) +
    geom_point(data = center.42, aes(x= x , y =y , color = "data.43")) +
    geom_point(data = center.72, aes(x= x , y =y , color = "data.72")) +
    xlim(-0.025,0.02) +  ylim(-0.025,0.02) +  ggtitle(sprintf("LHRS run %d",runID.step)) 
  myplots[[indexID]] = myp
}

myplots[1]
#> [[1]]

myplots[2]
#> [[1]]

myplots[3]
#> [[1]]

result.ph %>% filter(index == 63)%>%knitr::kable()
runID index aver_X aver_Y
2239 63 0.0009 4e-04
2240 63 -0.0001 4e-04
2241 63 -0.0011 2e-04 
result.ph %>% filter(index == 42)%>%knitr::kable()
runID index aver_X aver_Y
2239 42 -0.0028 0.0027
2240 42 -0.0044 0.0024
2241 42 -0.0054 0.0026 
result.ph %>% filter(index == 72)%>%knitr::kable()
runID index aver_X aver_Y
2239 72 0.0026 0.0029
2240 72 0.0014 0.0030
2241 72 0.0002 0.0033 
runList = c(2239,2240,2241)
result.y = tibble(runID = numeric(), index = numeric(), aver_X = numeric(), aver_Y = numeric())
colors <- c("col.4"="black", "col.6"="red", "col.7"="blue")
myplots <- list()  # new empty list
for (indexID in 1:length(runList)){
  runID.step = runList[indexID] 
  
  data.step = data_focal %>% filter(runID == runID.step) %>% filter(SieveColID == 4 | SieveColID == 6 | SieveColID == 7)
  
  data63 = data.step %>% filter(SieveColID == 6 & SieveRowID == 3)
  center.63 =  tibble(x= mean(data63$focal_y), y = mean(data63$focal_th))
  
  data42 = data.step %>% filter(SieveColID == 4 & SieveRowID == 2)
  center.42 =  tibble(x= mean(data42$focal_y), y = mean(data42$focal_th))
  data72 = data.step %>% filter(SieveColID == 7 & SieveRowID == 2)
  center.72 = tibble(x= mean(data72$focal_y), y = mean(data72$focal_th))

  result.y = add_row(result.y,tibble(runID = runID.step, index = 63, aver_X = center.63$x, aver_Y = center.63$y))
  result.y = add_row(result.y,tibble(runID = runID.step, index = 42, aver_X = center.42$x, aver_Y = center.42$y))
  result.y = add_row(result.y,tibble(runID = runID.step, index = 72, aver_X = center.72$x, aver_Y = center.72$y))
  
  myp = ggplot(data.step) + geom_bin2d(aes(x=focal_y,y=focal_th,color = sprintf("col.%d",SieveColID)),bins=300) + geom_point(data = center.63, aes(x= x , y =y , color = "data.63")) +
    geom_point(data = center.42, aes(x= x , y =y , color = "data.43")) +
    geom_point(data = center.72, aes(x= x , y =y , color = "data.72")) +
    xlim(-0.025,0.02) +  ylim(-0.025,0.02) +  ggtitle(sprintf("LHRS run %d",runID.step)) 
  myplots[[indexID]] = myp
}

myplots[1]
#> [[1]]
#> Warning: Removed 6 rows containing non-finite values (stat_bin2d).

myplots[2]
#> [[1]]
#> Warning: Removed 15 rows containing non-finite values (stat_bin2d).

myplots[3]
#> [[1]]
#> Warning: Removed 15 rows containing non-finite values (stat_bin2d).

result.y %>% filter(index==63) %>% knitr::kable()
runID index aver_X aver_Y
2239 63 0.0045 4e-04
2240 63 -0.0002 4e-04
2241 63 -0.0038 2e-04 
result.y %>% filter(index==42) %>% knitr::kable()
runID index aver_X aver_Y
2239 42 0.0142 0.0027
2240 42 0.0101 0.0024
2241 42 0.0061 0.0026 
result.y %>% filter(index==72) %>% knitr::kable()
runID index aver_X aver_Y
2239 72 0.0008 0.0029
2240 72 -0.0039 0.0030
2241 72 -0.0075 0.0033

Check All Y and \(\phi\) Parameters

Sieve ID name rule ## focal \(\phi\) and \(Y\) Parameters

runList = c(2239, 2240, 2241)
sieveList = c(42,53,63,72,83,93,102,113)

result.sieve = tibble(runID = runID.step,index = numeric(),aver_focal_ph = numeric(),aver_focal_th = numeric(),aver_focal_x = numeric(),
                      aver_focal_y = numeric(),theor_targ_th = numeric(), theor_targ_ph = numeric())
focal.ph.plot <- list()
focal.y.plot <- list()
targ.plot <- list()

for (indexID in 1:length(runList)) {
  runID.step = runList[indexID]
  # prepare the data
  data.step = data_focal %>% filter(runID == runID.step)
  data.step$colIndexer = sprintf("col.%d",data.step$SieveColID)
  for(sieveIndex in 1:length(sieveList)){
    rowID = sieveList[sieveIndex]%%10
    colID = sieveList[sieveIndex]%/%10
    #print(sprintf("%s -> %s : %s",sieveList[sieveIndex],colID,rowID))
    
    #load the dataset and write the information to the buffer
    # project the focal and target varaibles
    data.step.sieve = data.step %>% filter(SieveColID == colID & SieveRowID == rowID)
    sieve.mean = tibble(runID = runID.step, index = sieveList[sieveIndex],aver_focal_ph = mean(data.step.sieve$focal_ph),aver_focal_th = mean(data.step.sieve$focal_th),aver_focal_x = mean(data.step.sieve$focal_x), aver_focal_y = mean(data.step.sieve$focal_y),theor_targ_th = mean(data.step.sieve$targCalTh), theor_targ_ph= mean(data.step.sieve$targCalPh))
    result.sieve = add_row(result.sieve,sieve.mean)
  }  
  myp.th = ggplot(data.step) + geom_bin2d(aes(x=focal_ph,y=focal_th,color = colIndexer),bins=300) + geom_point(data = result.sieve%>% filter(runID == runID.step), aes(x= aver_focal_ph , y =aver_focal_th , color = "red")) +
    xlim(-0.025,0.02) +  ylim(-0.025,0.02) +  ggtitle(sprintf("LHRS run %d",runID.step)) 
  focal.ph.plot[[indexID]] = myp.th

  myp.y = ggplot(data.step) + geom_bin2d(aes(x=focal_y,y=focal_th,color = colIndexer),bins=300) + geom_point(data = result.sieve%>% filter(runID == runID.step), aes(x= aver_focal_y , y =aver_focal_th , color = "red")) +
    xlim(-0.025,0.02) +  ylim(-0.025,0.02) +  ggtitle(sprintf("LHRS run %d",runID.step)) 
  focal.y.plot[[indexID]] = myp.y
  
  myp.targ = ggplot(data.step) + geom_point(aes(x=targCalPh,y=targCalTh,color = colIndexer)) + 
    xlim(-0.025,0.025) +  ylim(-0.035,0.035) +  ggtitle(sprintf("LHRS run %d target Var",runID.step)) 
  targ.plot[[indexID]] = myp.targ
}

target variable check

targ.plot[1]
#> [[1]]

targ.plot[2]
#> [[1]]

targ.plot[3]
#> [[1]]

\(\phi\) result

focal.ph.plot[1]
#> [[1]]

focal.ph.plot[2]
#> [[1]]

focal.ph.plot[3]
#> [[1]]

## \(Y\) result

focal.y.plot[1]
#> [[1]]

focal.y.plot[2]
#> [[1]]

focal.y.plot[3]
#> [[1]]

focal plane variable and theoretical target position

result.sieve %>% select(-aver_focal_x, -aver_focal_th, -theor_targ_th)  %>% filter(index == 42)%>%knitr::kable()
runID index aver_focal_ph aver_focal_y theor_targ_ph
2239 42 -0.0028 0.0142 -0.0126
2240 42 -0.0044 0.0101 -0.0085
2241 42 -0.0054 0.0061 -0.0054 
result.sieve %>% select(-aver_focal_x, -aver_focal_th, -theor_targ_th)  %>% filter(index == 53)%>%knitr::kable()
runID index aver_focal_ph aver_focal_y theor_targ_ph
2239 53 -0.0021 0.0122 -0.0101
2240 53 -0.0034 0.0071 -0.0060
2241 53 -0.0044 0.0034 -0.0030 
result.sieve %>% select(-aver_focal_x, -aver_focal_th, -theor_targ_th)  %>% filter(index == 63)%>%knitr::kable()
runID index aver_focal_ph aver_focal_y theor_targ_ph
2239 63 0.0009 0.0045 -0.0039
2240 63 -0.0001 -0.0002 0.0002
2241 63 -0.0011 -0.0038 0.0033 
result.sieve %>% select(-aver_focal_x, -aver_focal_th, -theor_targ_th)  %>% filter(index == 72)%>%knitr::kable()
runID index aver_focal_ph aver_focal_y theor_targ_ph
2239 72 0.0026 0.0008 -0.0007
2240 72 0.0014 -0.0039 0.0033
2241 72 0.0002 -0.0075 0.0064 
result.sieve %>% select(-aver_focal_x, -aver_focal_th, -theor_targ_th)  %>% filter(index == 83)%>%knitr::kable()
runID index aver_focal_ph aver_focal_y theor_targ_ph
2239 83 0.0043 -0.0023 0.0024
2240 83 0.0035 -0.0069 0.0065
2241 83 0.0026 -0.0104 0.0095 
result.sieve %>% select(-aver_focal_x, -aver_focal_th, -theor_targ_th)  %>% filter(index == 93)%>%knitr::kable()
runID index aver_focal_ph aver_focal_y theor_targ_ph
2239 93 0.0086 -0.0085 0.0086
2240 93 0.0078 -0.0128 0.0127
2241 93 0.0072 -0.0161 0.0158 
result.sieve %>% select(-aver_focal_x, -aver_focal_th, -theor_targ_th)  %>% filter(index == 102)%>%knitr::kable()
runID index aver_focal_ph aver_focal_y theor_targ_ph
2239 102 0.0102 -0.0117 0.0118
2240 102 0.0092 -0.0159 0.0158
2241 102 0.0085 -0.0192 0.0189 
result.sieve %>% select(-aver_focal_x, -aver_focal_th, -theor_targ_th)  %>% filter(index == 113)%>%knitr::kable()
runID index aver_focal_ph aver_focal_y theor_targ_ph
2239 113 0.0134 -0.0143 0.0148
2240 113 0.0126 -0.0183 0.0189
2241 113 0.0121 -0.0213 0.0219